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Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations.

ABSTRACT: Large-scale genome rearrangements have been observed in cells adapting to various selective conditions during laboratory evolution experiments. However, it remains unclear whether these types of mutations can be stably maintained in populations and how they impact the evolutionary trajectories. Here we show that chromosomal rearrangements contribute to extremely high copper tolerance in a set of natural yeast strains isolated from Evolution Canyon (EC), Israel. The chromosomal rearrangements in EC strains result in segmental duplications in chromosomes 7 and 8, which increase the copy number of genes involved in copper regulation, including the crucial transcriptional activator CUP2 and the metallothionein CUP1. The copy number of CUP2 is correlated with the level of copper tolerance, indicating that increasing dosages of a single transcriptional activator by chromosomal rearrangements has a profound effect on a regulatory pathway. By gene expression analysis and functional assays, we identified three previously unknown downstream targets of CUP2: PHO84, SCM4, and CIN2, all of which contributed to copper tolerance in EC strains. Finally, we conducted an evolution experiment to examine how cells maintained these changes in a fluctuating environment. Interestingly, the rearranged chromosomes were reverted back to the wild-type configuration at a high frequency and the recovered chromosome became fixed in less selective conditions. Our results suggest that transposon-mediated chromosomal rearrangements can be highly dynamic and can serve as a reversible mechanism during early stages of adaptive evolution.

SUBMITTER: Chang SL

PROVIDER: S-EPMC3554576 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations.

Chang Shang-Lin SL Lai Huei-Yi HY Tung Shu-Yun SY Leu Jun-Yi JY

PLoS genetics 20130124 1

Large-scale genome rearrangements have been observed in cells adapting to various selective conditions during laboratory evolution experiments. However, it remains unclear whether these types of mutations can be stably maintained in populations and how they impact the evolutionary trajectories. Here we show that chromosomal rearrangements contribute to extremely high copper tolerance in a set of natural yeast strains isolated from Evolution Canyon (EC), Israel. The chromosomal rearrangements in ...[more]

PMID: 23358723

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Project description:BackgroundRecently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space.ResultsIn this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into [Formula: see text] subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to [Formula: see text] families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with [Formula: see text], when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the [Formula: see text] algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with [Formula: see text]. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc .

Dataset Information

Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations.

Publications

Dynamic large-scale chromosomal rearrangements fuel rapid adaptation in yeast populations.

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